Title :
A Sequential Framework for Image Change Detection
Author :
Lingg, Andrew J. ; Zelnio, Edmund ; Garber, Fred ; Rigling, Brian D.
Author_Institution :
Wright State Univ., Dayton, OH, USA
Abstract :
We present a sequential framework for change detection. This framework allows us to use multiple images from reference and mission passes of a scene of interest in order to improve detection performance. It includes a change statistic that is easily updated when additional data becomes available. Detection performance using this statistic is predictable when the reference and image data are drawn from known distributions. We verify our performance prediction by simulation. Additionally, we show that detection performance improves with additional measurements on a set of synthetic aperture radar images and a set of visible images with unknown probability distributions.
Keywords :
feature extraction; image sequences; object detection; probability; radar imaging; synthetic aperture radar; detection performance; image change detection; image data; multiple images; performance prediction; sequential framework; synthetic aperture radar images; unknown probability distributions; visible images; Computational modeling; Data models; Materials; Noise; Predictive models; Probability; Probability density function; Image analysis; image sequence analysis; subtraction techniques;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2014.2309432
